Time-series data is relentless and grows at an ever-increasing rate, becoming expensive and unwieldy to store and query. Managing time-series data is essential for building high-performance and cost-effective applications. Timescale provides the tools needed to maintain storage and query performance without requiring data deletion. The five steps to "data lifecycle management" include ingesting and storing data efficiently, querying recent raw data regularly, creating aggregated historical rollups, archiving/data tiering older raw data, and dropping raw data after some predefined interval passes. Time-series data poses a unique challenge to managing data due to its overwhelming nature, requiring features like native compression, continuous aggregates, and data tiering. TimescaleDB offers built-in features such as hypertables, chunks, compression, continuous aggregates, and user-defined actions to manage time-series data effectively. By implementing these features, users can reduce costs, improve performance, and maintain efficient query performance.